DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is responsive to application filed on 09/15/2022.
Claims 1-8 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/15/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The abstract of the disclosure is objected to because it exceeds 150 words in length. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, and 3-8 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1, and 3-8 of U.S. Patent No. 11, 996, 984 B2 as follows:
Instant Application No. 17/906, 365
Patent No. 11, 996, 984 B2
1. A computer implemented method to model physical infrastructure of a transmission network for a utility service, the physical infrastructure including a set of physical components in the transmission network, the method comprising:
accessing each of a plurality of physical infrastructure data sources, each data source including records each storing information on at least a subset of the set of physical components including a location and a type of each physical component in the subset, wherein each record has associated an indication of a degree of confidence of an accuracy of the record;
generating a model of the physical infrastructure including an indication of a location and a type of physical components based on the data sources, wherein records of the data sources having common location and type are aggregated for indication in the model;
associating each indication in the model with a degree of confidence of accuracy of the indication based on the degree of confidence information from the data sources;
accessing a set of rules defining relationships between types of physical components; and
refining the model based on the set of rules including adjusting the degree of confidence of accuracy of indications in the model based on satisfaction of the set of rules.
1. A computer implemented method to specify a software defined network (SDN) for deployment by an SDN controller, the SDN controller being adapted to configure a plurality of physical network components to deliver communication services, the method comprising:
generating a model of the physical network components by:
accessing each of a plurality of data sources, each of the plurality of data sources including records each storing information on at least a subset of the plurality of physical network components including a location and a type of each physical network component in the subset and interconnections between the physical network components, wherein each record has associated an indication of a degree of confidence of an accuracy of the record,
defining the model including an indication of the location, the type and the interconnections of the physical network components based on the plurality of data sources, wherein the records of the data sources having a common location and a common type are aggregated for indication in the model,
associating each indication in the model with a degree of confidence of an accuracy of the indication based on the degree of confidence of the accuracy of the record,
accessing a set of rules defining relationships between types of physical component, and
refining the model based on the rules including adjusting the degree of confidence of the accuracy of indications in the model based on satisfaction of the rules; and selecting a subset of physical network components in the refined model for inclusion in the SDN specification.
3. The method of claim 1, further comprising triggering a survey process for a subset of physical components in the transmission network, the subset of physical components corresponding to indications in the refined model having a degree of confidence of accuracy meeting a predetermined threshold degree of confidence.
3. The method of claim 1, further comprising triggering a survey process for a subset of the physical network components in the physical network, the subset corresponding to indications in the refined model having a degree of confidence of accuracy meeting a predetermined threshold degree of confidence.
4. The method of claim 3, wherein the survey process includes one or more of: a physical discovery process, or an imaging process.
4. The method of claim 3, wherein the survey process includes one or more of: a physical discovery process; and an imaging process.
5. The method of claim 1, wherein refining the model based on the rules includes: inferring an additional physical component including an inferred location and type of the additional physical component and adding an indication for the additional physical component to the model, the additional physical component being inferred based on the set of rules and a subset of the indications in the model; and associating a degree of confidence of accuracy of the indication for the additional physical component based on a degree of confidence associated with at least some indications in the subset of indications.
5. The method of claim 1, wherein refining the model based on the rules includes: inferring an additional physical network component including an inferred location and an inferred type of the additional physical network component and adding an indication for the additional physical network component to the model, the additional physical network component being inferred based on the rules and a subset of the indications in the model; and associating a degree of confidence of accuracy of the indication for the additional physical network component based on a degree of confidence associated with at least some indications in the subset of indications.
6. The method of claim 1, wherein the records included in at least a subset of the data sources include a status indication for at least a subset of the physical components, the status indication identifying a state of a physical component as one or more of: an operational state, or a configuration state if the physical component.
6. The method of claim 1, wherein the records included in at least a subset of the data sources include a status indication for at least a subset of the physical network components, the status indication identifying a state of a physical network component as one or more of: an operational state; and a configuration state of the physical component.
7. A computer system comprising: a processor and memory storing computer program code for modeling physical infrastructure of a transmission network for a utility service, the physical infrastructure including a set of physical components in the transmission network, by: accessing each of a plurality of physical infrastructure data sources, each data source including records each storing information on at least a subset of the set of physical components including a location and a type of each physical component in the subset, wherein each record has associated an indication of a degree of confidence of an accuracy of the record; generating a model of the physical infrastructure including an indication of a location and a type of physical components based on the data sources, wherein records of the data sources having common location and type are aggregated for indication in the model; associating each indication in the model with a degree of confidence of accuracy of the indication based on the degree of confidence information from the data sources; accessing a set of rules defining relationships between types of physical components; and refining the model based on the set of rules including adjusting the degree of confidence of accuracy of indications in the model based on satisfaction of the set of rules.
7. A computer system comprising: a processor and memory storing computer program code for specifying a software defined network (SDN) for deployment by an SDN controller, the SDN controller being adapted to configure a plurality of physical network components to deliver communication services, by: generating a model of the physical network components by: accessing each of a plurality of data sources, each of the plurality of data sources including records each storing information on at least a subset of the plurality of physical network components including a location and a type of each physical network component in the subset and interconnections between the physical network components, wherein each record has associated an indication of a degree of confidence of an accuracy of the record, defining the model including an indication of the location, the type and the interconnections of the physical network components based on the plurality of data sources, wherein the records of the data sources having a common location and a common type are aggregated for indication in the model, associating each indication in the model with a degree of confidence of an accuracy of the indication based on the degree of confidence of the accuracy of the record, accessing a set of rules defining relationships between types of physical components, and refining the model based on the rules including adjusting the degree of confidence of the accuracy of indications in the model based on satisfaction of the rules; and selecting a subset of physical network components in the refined model for inclusion in the SDN specification.
8. A non-transitory computer-readable storage medium storing computer program code to, when loaded into a computer system and executed thereon, cause the computer system to model physical infrastructure of a transmission network for a utility service, the physical infrastructure including a set of physical components in the transmission network, by: accessing each of a plurality of physical infrastructure data sources, each data source including records each storing information on at least a subset of the set of physical components including a location and a type of each physical component in the subset, wherein each record has associated an indication of a degree of confidence of an accuracy of the record; generating a model of the physical infrastructure including an indication of a location and a type of physical components based on the data sources, wherein records of the data sources having common location and type are aggregated for indication in the model; associating each indication in the model with a degree of confidence of accuracy of the indication based on the degree of confidence information from the data sources; accessing a set of rules defining relationships between types of physical components; and refining the model based on the set of rules including adjusting the degree of confidence of accuracy of indications in the model based on satisfaction of the set of rules.
8. A non-transitory computer-readable storage medium storing computer program code to, when loaded into a computer system and executed thereon, cause the computer system to specify a software defined network (SDN) for deployment by an SDN controller, the SDN controller being adapted to configure a plurality of physical network components to deliver communication services, by: generating a model of the physical network components by: accessing each of a plurality of data sources, each of the plurality of data sources including records each storing information on at least a subset of the plurality of physical network components including a location and a type of each physical network component in the subset and interconnections between the physical network components, wherein each record has associated an indication of a degree of confidence of an accuracy of the record, defining the model including an indication of the location, the type and the interconnections of the physical network components based on the plurality of data sources, wherein the records of the data sources having a common location and a common type are aggregated for indication in the model, associating each indication in the model with a degree of confidence of an accuracy of the indication based on the degree of confidence of the accuracy of the record, accessing a set of rules defining relationships between types of physical components, and refining the model based on the rules including adjusting the degree of confidence of the accuracy of indications in the model based on satisfaction of the rules; and selecting a subset of physical network components in the refined model for inclusion in the SDN specification.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (Does this claim fall within at least one statutory category?):
Claims 1-6 are directed to a method.
Claim 7 is directed to a system.
Claim 8 is directed to a product.
Therefore, claims 1-8 fall into at least one of the four statutory categories.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 1:
A computer implemented method to model physical infrastructure of a transmission network for a utility service, the physical infrastructure including a set of physical components in the transmission network, the method comprising:
accessing each of a plurality of physical infrastructure data sources [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)], each data source including records each storing information on at least a subset of the set of physical components including a location and a type of each physical component in the subset [a generic computer element for performing a generic computer functions], wherein each record has associated an indication of a degree of confidence of an accuracy of the record [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)];
generating a model of the physical infrastructure including an indication of a location and a type of physical components based on the data sources, wherein records of the data sources having common location and type are aggregated for indication in the model [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)];
associating each indication in the model with a degree of confidence of accuracy of the indication based on the degree of confidence information from the data sources [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)];
accessing a set of rules defining relationships between types of physical components [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)]; and
refining the model based on the set of rules including adjusting the degree of confidence of accuracy of indications in the model based on satisfaction of the set of rules [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): There is no any additional elements that integrate into practical application.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): There is no any additional elements that amount to significantly more than the judicial exception.
As per claim 2, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion).
As per claim 3, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion).
As per claim 4, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion).
As per claim 5, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion).
As per claim 6, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion).
As per Claim 7, independent claim 7 recites limitations analogous in scope to those of independent claim 1, and as such are similarly rejected. Further, claim 15 recites additional elements of “processor” and “memory”. The “processor” and “memory” recited at a high level of generality (e.g. a generic computer element for performing a generic computer functions) such that it amounts to no more than mere application of the judicial exception using generic computer component(s). Accordingly, the additional element(s) of each of these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, as discussed above with respect to the integration of the abstract into a practical application, the additional elements of “processor” and “memory” amount to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
As per Claim 8, independent claim 8 recites limitations analogous in scope to those of independent claim 1, and as such are similar rejected.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2 and 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2009/0238079 A1 issued to Gantenbein et al in view of Zedler et al (Michel Zedler, Dieter Gantenbein, “Physical location awareness for enterprise IT assets”, 2008 IEEE, pgs. 1-6).
1. Gantenbein et al discloses a computer implemented method to model physical infrastructure of a transmission network for a utility service, the physical infrastructure including a set of physical components in the transmission network, the method comprising:
accessing each of a plurality of physical infrastructure data sources, each data source including records each storing information on at least a subset of the set of physical components including a location and a type of each physical component in the subset, wherein each record has associated an indication of a degree of confidence of an accuracy of the record (See: [0042] FIG. 2A shows a generalized calibration phase according to an embodiment of the invention. In this example, two inventory record location observations (202) are integrated with three topology elements (top1, top2, top3) using network information (201) and asset information (203) analogous to the initial example. As a difference, top1 is found to be associated to two devices in two different locations (loc1 and loc2). Now, instead of replacing the location mapping for top1 during the sequential processing of inventory records, we store any observed location for top1 and apply the SLV algorithm (explained in the following) in order to determine the consenting location (loc3) and a confidence metric (conf1); [0044] In order to realize the described behavior, we utilize two core functional components: [0045] An IT asset configuration and network connectivity model (CCM) which is continuously updated through network monitoring techniques, enabling the combination of topology and location information during calibration and estimation phases);
generating a model of the physical infrastructure including an indication of a location and a type of physical components based on the data sources, wherein records of the data sources having common location and type are aggregated for indication in the model (See: [0045] An IT asset configuration and network connectivity model (CCM) which is continuously updated through network monitoring techniques, enabling the combination of topology and location information during calibration and estimation phases; [0046] A semantic spatial model (SSM) of enterprise locations, enabling the combination of multiple location findings during calibration and estimation phases; [0047] 1. Configuration and Connectivity Model (CCM); [0048] The first pillar of the location system is a continuously updated model of network device to topology element associations. The more accurate and complete the CCM information, the more efficient calibration and location estimation, both gaining robustness and confidence. Therefore, besides employing conventional network monitoring techniques from the network infrastructure side (SNMP), we also use target device side software agents to update locally observed network attributes. This model can be queried for physical and logical associations between target devices and topology elements. Further, model transformations are persisted in a change log, to enable historic state queries);
associating each indication in the model with a degree of confidence of accuracy of the indication based on the degree of confidence information from the data sources (See: [0012] The location mappings generating step may further include computing respective location confidence measures for a sample topology element that has more than one location. When the sample topology element has more than one location, the location with the highest confidence measure is selected as the learned location of the sample topology element; [0042] FIG. 2A shows a generalized calibration phase according to an embodiment of the invention. In this example, two inventory record location observations (202) are integrated with three topology elements (top1, top2, top3) using network information (201) and asset information (203) analogous to the initial example. As a difference, top1 is found to be associated to two devices in two different locations (loc1 and loc2). Now, instead of replacing the location mapping for top1 during the sequential processing of inventory records, we store any observed location for top1 and apply the SLV algorithm (explained in the following) in order to determine the consenting location (loc3) and a confidence metric (conf1). Hence, we generally do not assume topology elements to relate to distinct locations as in the above example; [0043] FIG. 2B shows a generalized estimation step according to an embodiment of the invention. The location mappings (204) produced by the calibration phase are used to guess the physical location of arbitrary network devices in a way that high confidence mappings dominate low confidence mappings. For this purpose, we attempt to identify any associated topology element for the target device (201') and then determine the location consent of any applicable mappings (204) with the help of the SLV algorithm. In this estimation step, the SLV algorithm considers the heterogeneous confidence of location mappings in a weighted voting. In practice, calibration and estimation steps can be performed interweaved concurrently);
accessing a set of rules defining relationships between types of physical components (See: par [0043] The location mappings (204) produced by the calibration phase are used to guess the physical location of arbitrary network devices in a way that high confidence mappings dominate low confidence mappings. For this purpose, we attempt to identify any associated topology element for the target device (201') and then determine the location consent of any applicable mappings (204) with the help of the SLV algorithm. In this estimation step, the SLV algorithm considers the heterogeneous confidence of location mappings in a weighted voting. In practice, calibration and estimation steps can be performed interweaved concurrently; [0072] Location estimation is based on association rules. Association rules are a common concept in data mining. They express regularities underlying a dataset and can be used for prediction); and
refining the model based on the set of rules (See: [0072] Location estimation is based on association rules. Association rules are a common concept in data mining. They express regularities underlying a dataset and can be used for prediction; [0073] The unknown location L.sub.t of a target device t is estimated by its associated topology element set T.sub.t. t:(T.sub.t,L.sub.t); T.sub.t and L.sub.t are defined analog to T.sub.e and L.sub.e. For our purpose, we define a set of association rules R, where each rule: r:S.sub.t,S.sub.t.epsilon.P(T.sub.t),.epsilon..LAMBDA predicts from a topology element set S.sub.t a semantic location in the SSM; [0074] Before explaining the algorithm, we introduce a number of definitions related to association rules: [0075] 1) The coverage (or support) of an association rule is defined as the number of instances for which its prediction is correct. The training set produced by the examples yields, for each rule, a coverage: cov(r)=|{e|e.epsilon.E,S.sub.t.OR right.T.sub.e.epsilon.L.sub.e}|)).
Gantenbein et al does not disclose but Zedler et al discloses adjusting the degree of confidence of accuracy of indications in the model based on satisfaction of the set of rules (See: pg. 3, right side column, use the Simple Network Management Protocol (SNMP) to access credential-protected, operational network data that is internally kept by network routers and switches in order to extract IP to MAC address and MAC address to switch port mappings. Changes in this data continuously trigger an update of the CM state. The CM defines the set of topology elements Θ, provides unique identifiers for topology elements and target devices, and can hence be queried for current, last seen, or historically established device associations. As the completeness of the CM state determines the accuracy and precision of location estimation, the CM can optionally be updated by local software agents, which reside on the target device side and monitor local routing table and IP information; pg. 4 left side column, 2) The accuracy of an association rule is defined as the number of instances the rule predicts correctly (which is the coverage), expressed as a proportion of all instances; pg. 5 left side column, the application can also monitor local device connectivity, update the CM, and thus improve the accuracy of the training set. In return, the user benefits from the automatic location discovery and sharing capabilities; pg. 5 left side column, The information, which is primarily used to provide contextual information on the physical presence of individual users, can hence be leveraged for system calibration. Further, the application can also monitor local device connectivity, update the CM, and thus improve the accuracy of the training set. In return, the user benefits from the automatic location discovery and sharing capabilities; pg. 5 right side column, s (a) actually possible to maintain an accurate model of dynamic associations between devices and the fixed topology in a productive IT environment, and that (b) training set generation by external sources is feasible).
It would have been obvious before the effective filing date to combine automatically discover and track the physical location of networked devices as taught by Zedler et al to computing system networks method of Gantenbein et al would be to provide up-to-date location meta-data of sufficient granularity and in an appropriate spatial reference system (Gantenbein et al, par [0008]).
2. Gantenbein et al discloses the method of claim 1 further comprising defining a deployment specification for one or more new physical components in the transmission network by determining a location and a type of each new physical component based on the refined model (See: [0060] As shown in FIGS. 3A through 3C, a particular topology element top1 may be associated with different location nodes in the spatial model over time, and in particular with each new location finding being processed).
5. Gantenbein et al discloses the method of claim 1 wherein refining the model based on the rules includes the steps of: inferring an additional physical component including an inferred location and type of the additional physical component and adding an indication for the additional physical component to the model, the additional physical component being inferred based on the set of rules and a subset of the indications in the model (See: [0072] Location estimation is based on association rules. Association rules are a common concept in data mining. They express regularities underlying a dataset and can be used for prediction; [0073] The unknown location L.sub.t of a target device t is estimated by its associated topology element set T.sub.t. t:(T.sub.t,L.sub.t); T.sub.t and L.sub.t are defined analog to T.sub.e and L.sub.e. For our purpose, we define a set of association rules R, where each rule: r:S.sub.t,S.sub.t.epsilon.P(T.sub.t),.epsilon..LAMBDA predicts from a topology element set S.sub.t a semantic location in the SSM; [0074] Before explaining the algorithm, we introduce a number of definitions related to association rules: [0075] 1) The coverage (or support) of an association rule is defined as the number of instances for which its prediction is correct. The training set produced by the examples yields, for each rule, a coverage: cov(r)=|{e|e.epsilon.E,S.sub.t.OR right.T.sub.e.epsilon.L.sub.e}|)); and associating a degree of confidence of accuracy of the indication for the additional physical component based on a degree of confidence associated with at least some indications in the subset of indications (See: [0012] The location mappings generating step may further include computing respective location confidence measures for a sample topology element that has more than one location. When the sample topology element has more than one location, the location with the highest confidence measure is selected as the learned location of the sample topology element; [0042] FIG. 2A shows a generalized calibration phase according to an embodiment of the invention. In this example, two inventory record location observations (202) are integrated with three topology elements (top1, top2, top3) using network information (201) and asset information (203) analogous to the initial example. As a difference, top1 is found to be associated to two devices in two different locations (loc1 and loc2). Now, instead of replacing the location mapping for top1 during the sequential processing of inventory records, we store any observed location for top1 and apply the SLV algorithm (explained in the following) in order to determine the consenting location (loc3) and a confidence metric (conf1). Hence, we generally do not assume topology elements to relate to distinct locations as in the above example; [0043] FIG. 2B shows a generalized estimation step according to an embodiment of the invention. The location mappings (204) produced by the calibration phase are used to guess the physical location of arbitrary network devices in a way that high confidence mappings dominate low confidence mappings. For this purpose, we attempt to identify any associated topology element for the target device (201') and then determine the location consent of any applicable mappings (204) with the help of the SLV algorithm. In this estimation step, the SLV algorithm considers the heterogeneous confidence of location mappings in a weighted voting. In practice, calibration and estimation steps can be performed interweaved concurrently).
6. Gantenbein et al discloses the method of claim 1 wherein the records included in at least a subset of the data sources include a status indication for at least a subset of the physical components, the status indication identifying a state of a physical component as one or more of: an operational state, or a configuration state if the physical component (See:[0045] An IT asset configuration and network connectivity model (CCM) which is continuously updated through network monitoring techniques, enabling the combination of topology and location information during calibration and estimation phases).
As per Claims 7-8: The instant claims recite substantially same limitation as the above rejected claim 1, and therefore rejected under the same rationale.
Claims 3, and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Gantenbein et al and Zedler et al as applied to claim 1 above, and further in view of US Publication No. 2020/0067880 issued to Kim et al.
3. Gantenbein et al discloses the method of claim 1.
Gantenbein et al does not disclose but Zedler et al discloses triggering a survey process for a subset of physical components in the transmission network, the subset of physical components corresponding to indications in the refined model having a degree of confidence of accuracy (See: pg. 3 right side column, We use the Simple Network Management Protocol (SNMP) to access credential-protected, operational network data that is internally kept by network routers and switches in order to extract IP to MAC address and MAC address to switch port mappings. Changes in this data continuously trigger an update of the CM state. The CM defines the set of topology elements Θ, provides unique identifiers for topology elements and target devices, and can hence be queried for current, last seen, or historically established device associations. As the completeness of the CM state determines the accuracy and precision of location estimation, the CM can optionally be updated by local software agents, which reside on the target device side and monitor local routing table and IP information).
It would have been obvious before the effective filing date to combine automatically discover and track the physical location of networked devices as taught by Zedler et al to computing system networks method of Gantenbein et al would be to provide up-to-date location meta-data of sufficient granularity and in an appropriate spatial reference system (Gantenbein et al, par [0008]).
Neither the reference disclose but Kim et al discloses meeting a predetermined threshold degree of confidence (See: [0058] In one example, the processor 122 can determine the current system context 128 by (i) determining, based on the network traffic information (e.g., the network traffic information 352A, 352B), a confidence metric for each of the plurality of system contexts 128, (ii) performing a comparison of each confidence metric to a threshold value, and (iii) determining, based on the comparison, that the confidence metric for the current system context 128 is greater than the threshold value).
It would have been obvious before the effective filing date to combine communication network as taught by Kim et al to computing system networks method of Gantenbein et al would be to mitigate cyber security threats to devices within the network and/or prevent unauthorized access to network resources (Kim et al, par [0002]).
4. Gantenbein et al discloses the method of claim 3. wherein the survey process includes one or more of: a physical discovery process, or an imaging process (See: [0011] The location learning step may be performed in accordance with one or more network monitoring techniques. The one or more network monitoring techniques may include capturing location knowledge using one or more of: (i) a physical inventory; (ii) a demographic declaration; and (iii) a behavioral pattern).
Conclusion
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 12/04/2025